Heuristic‐Enhanced <scp>ILP</scp> Process Discovery With Multidimensional Dependency Filtering

ABSTRACT Process discovery aims to construct business process models from event data recorded by information systems. Traditional integer linear programming (ILP)‐based process discovery methods can extract complex local and nonlocal process structures. However, they often generate overfitted models when dealing with large‐scale logs, and ILP solvers are computationally inefficient. Moreover, event logs inevitably contain noise or outliers, and existing ILP methods struggle to distinguish between effective infrequent behaviors and noise. To address these challenges, this paper proposes a heuristic‐enhanced ILP‐based process discovery method that integrates region‐based constraint formulation and a dynamic threshold mechanism guided by multidimensional dependency metrics for effective noise filtering and model optimization. The method begins with heuristic preprocessing of the event logs, followed by constructing and solving region‐based ILP constraints to obtain an initial Petri net process model. Next, model structure replay and ILP are applied for preliminary noise filtering, and multidimensional dependency metrics are used to compute comprehensive dependency scores. Dynamic thresholds are then applied to further filter noise, enabling iterative optimization of the Petri net model. Finally, experimental evaluations on real and synthetic event logs demonstrate that the proposed method not only preserves effective infrequent behaviors but also significantly improves the quality of the discovered process models.